Background

Detection of a small number of circulating tumor cells is important, especially at the early stages of cancer. The small number of CTCs is hard to detect as very few approaches are sensitive enough to differentiate these from the pool of other cells. Improving the affinity of a selective surface-functionalized molecule is important given the sparsity of CTCs in vivo. There are a number of proteins and aptamers that provide such a high affinity but using a surface nano-texturing increases this affinity even further.

Method

This work reports an approach to improve affinity of tumor cell capture by using novel aptamers against cell-membrane over-expressed Epidermal Growth Factor Receptors (EGFR) on a nano-textured polydimethylsiloxane (PDMS) substrate. Surface immobilized aptamers are used to specifically capture tumor cells from physiological samples.

Results

The nano-texturing of PDMS increased surface roughness at the nanoscale. This increased the effective surface area and resulted in a significantly higher degree of surface functionalization. The phenomenon resulted in increased density of immobilized EGFR specific RNA aptamer molecules and provided significantly higher efficiency to capture cancer cells from a mixture. The data showed that CTCs could be captured and enriched leading to higher yield, yet higher background.

Conclusion

The comparison of glass slides, plain PDMS and nano-textured PDMS functionalized with aptamers show that a two-fold approach of using aptamers on nano-textured PDMS can be an important factor for cancer cytology devices especially for the idea of lab-on-chip towards higher yield in capture efficiency.

It has been hypothesized that components of enzymatic pathways might organize into intracellular assemblies to improve their catalytic efficiency or lead to coordinate regulation. Accordingly, de novo purine biosynthesis enzymes may form a purinosome in the absence of purines, and a punctate intracellular body has been identified as the purinosome. We investigated the mechanism by which human de novo purine biosynthetic enzymes might be organized into purinosomes, especially under differing cellular conditions. Irregardless of the activity of bodies formed by endogenous enzymes, we demonstrate that intracellular bodies formed by transiently transfected, fluorescently tagged human purine biosynthesis proteins are best explained as protein aggregation.

Analysis of a genetic module repurposed between yeast and vertebrates reveals that a common antifungal medication is also a potent vascular disrupting agent.

Studies in diverse organisms have revealed a surprising depth to the evolutionary conservation of genetic modules. For example, a systematic analysis of such conserved modules has recently shown that genes in yeast that maintain cell walls have been repurposed in vertebrates to regulate vein and artery growth. We reasoned that by analyzing this particular module, we might identify small molecules targeting the yeast pathway that also act as angiogenesis inhibitors suitable for chemotherapy. This insight led to the finding that thiabendazole, an orally available antifungal drug in clinical use for 40 years, also potently inhibits angiogenesis in animal models and in human cells. Moreover, in vivo time-lapse imaging revealed that thiabendazole reversibly disassembles newly established blood vessels, marking it as vascular disrupting agent (VDA) and thus as a potential complementary therapeutic for use in combination with current anti-angiogenic therapies. Importantly, we also show that thiabendazole slows tumor growth and decreases vascular density in preclinical fibrosarcoma xenografts. Thus, an exploration of the evolutionary repurposing of gene networks has led directly to the identification of a potential new therapeutic application for an inexpensive drug that is already approved for clinical use in humans.

Author Summary

Yeast cells and vertebrate blood vessels would not seem to have much in common. However, we have discovered that during the course of evolution, a group of proteins whose function in yeast is to maintain cell walls has found an alternative use in vertebrates regulating angiogenesis. This remarkable repurposing of the proteins during evolution led us to hypothesize that, despite the different functions of the proteins in humans compared to yeast, drugs that modulated the yeast pathway might also modulate angiogenesis in humans and in animal models. One compound seemed a particularly promising candidate for this sort of approach: thiabendazole (TBZ), which has been in clinical use as a systemic antifungal and deworming treatment for 40 years. Gratifyingly, our study shows that TBZ is indeed able to act as a vascular disrupting agent and an angiogenesis inhibitor. Notably, TBZ also slowed tumor growth and decreased vascular density in human tumors grafted into mice. TBZ’s historical safety data and low cost make it an outstanding candidate for translation to clinical use as a complement to current anti-angiogenic strategies for the treatment of cancer. Our work demonstrates how model organisms from distant branches of the evolutionary tree can be exploited to arrive at a promising new drug.

A novel saccharide host containing four boronic acid recognition units on a single DNA duplex terminus was constructed. This construct allowed boronic acid sugar recognition in the context of double stranded DNA to be established while highlighting the benefits of multivalency. Following the solid-phase synthesis of a bis-boronic acid tag, two end-functionalized oligonucleotides with complementary sequences were functionalized through amide ligation. By annealing the boronic acid-DNA conjugates, a tetra-boronic acid DNA duplex was assembled. The saccharide binding ability of this tetra-boronic acid host was revealed through cellulose paper chromatography in the absence and presence of various saccharides. While no appreciable saccharide binding was seen in the case of a bis-boronic DNA conjugate, the increased migration of the tetra-boronic acid host relative to the control sequences in the presence of selected monosaccharides underscored the importance of multivalent effects. We thus identified a requirement for multiple recognition sites in these conjugate systems and expect the results to facilitate future efforts toward applying synthetic recognition systems to the realm of macromolecules.

In order to automate the optimization of complex biochemical and molecular biology reactions, we developed a Sequential Injection Analysis (SIA) device and combined this with a Design of Experiment (DOE) algorithm. This combination of hardware and software automatically explores the parameter space of the reaction and provides continuous feedback for optimizing reaction conditions. As an example, we optimized the endonuclease digest of a fluorogenic substrate, and showed that the optimized reaction conditions also applied to the digest of the substrate outside of the device, and to the digest of a plasmid. The sequential technique quickly arrived at optimized reaction conditions with less reagent use than a batch process (such as a fluid handling robot exploring multiple reaction conditions in parallel) would have. The device and method should now be amenable to much more complex molecular biology reactions whose variable spaces are correspondingly larger.

We have developed a selection scheme to generate nucleic acid sequences that recognize and directly internalize into mammalian cells without the aid of conventional delivery methods. To demonstrate the generality of the technology, two independent selections with different starting pools were performed against distinct target cells. Each selection yielded a single highly functional sequence, both of which folded into a common core structure. This internalization signal can be adapted for use as a general purpose reagent for transfection into a wide variety of cell types including primary cells.

A microfluidic device has been constructed to carry out the automated, continuous evolution of ribozymes. A comparison with manual efforts reveals that both are capable of far flung forays into sequence space.

Summary of recent advances (abstract)

Nucleic acid-based nanotechnology has always been perceived as novel, but has begun to move from theoretical demonstrations to practical applications. In particular, the large address spaces available to nucleic acids can be exploited to encode algorithms and/or act as circuits, and thereby process molecular information. In this review we revisit several milestones in the field of nucleic acid-based computation, but also highlight how the prospects for nucleic acid computation go beyond just a large address space. Functional nucleic acid elements (aptamers, ribozymes, and deoxyribozymes) can serve as inputs and outputs to the environment, and can act as logical elements. Into the future, the chemical dynamics of nucleic acids may prove as useful as hybridization for computation.

Signal amplification is a key component of molecular detection. Enzyme-free signal amplification is especially appealing for the development of low-cost, point-of-care diagnostics. It has been previously shown that enzyme-free DNA circuits with signal-amplification capacity can be designed using a mechanism called ‘catalyzed hairpin assembly’. However, it is unclear whether the efficiency and modularity of such circuits is suitable for multiple analytical applications. We have therefore designed and characterized a simplified DNA circuit based on catalyzed hairpin assembly, and applied it to multiple different analytical formats, including fluorescent, colorimetric, and electrochemical and signaling. By optimizing the design of previous hairpin-based catalytic assemblies we found that our circuit has almost zero background and a high catalytic efficiency, with a kcat value above 1 min−1. The inherent modularity of the circuit allowed us to readily adapt our circuit to detect both RNA and small molecule analytes. Overall, these data demonstrate that catalyzed hairpin assembly is suitable for analyte detection and signal amplification in a ‘plug-and-play’ fashion.

Aptamers continue to receive interest as potential therapeutic agents for the treatment of diseases, including cancer. In order to determine whether aptamers might eventually prove to be as useful as other clinical biopolymers, such as antibodies, we selected aptamers against an important clinical target, human epidermal growth factor receptor (hEGFR). The initial selection yielded only a single clone that could bind to hEGFR, but further mutation and optimization yielded a family of tight-binding aptamers. One of the selected aptamers, E07, bound tightly to the wild-type receptor (Kd = 2.4 nM). This aptamer can compete with EGF for binding, binds to a novel epitope on EGFR, and also binds a deletion mutant, EGFRvIII, that is commonly found in breast and lung cancers, and especially in grade IV glioblastoma multiforme, a cancer which has for the most part proved unresponsive to current therapies. The aptamer binds to cells expressing EGFR, blocks receptor autophosphorylation, and prevents proliferation of tumor cells in three-dimensional matrices. In short, the aptamer is a promising candidate for further development as an anti-tumor therapeutic. In addition, Aptamer E07 is readily internalized into EGFR-expressing cells, raising the possibility that it might be used to escort other anti-tumor or contrast agents.

Infrared multiphoton dissociation (IRMPD) on a linear ion trap mass spectrometer is applied for the sequencing of small interfering RNA (siRNA). Both single-strand siRNAs and duplex siRNA were characterized by IRMPD, and the results were compared to that obtained by traditional ion trap based collision induced dissociation (CID). The single-strand siRNA anions were observed to dissociate via cleavage of the 5’ P–O bonds yielding c- and y-type product ions as well as undergo neutral base loss. Full sequence coverage of the siRNA anions was obtained by both IRMPD and CID. While the CID mass spectra were dominated by base loss ions, accounting for ~25 – 40% of the product ion current, these ions were eliminated through secondary dissociation by increasing the irradiation time in the IRMPD mass spectra to produce higher abundances of informative sequence ions. With longer irradiation times, however, internal ions corresponding to cleavage of two 5’ P–O bonds began to populate the product ion mass spectra as well as higher abundances of [a - Base] and w-type ions. IRMPD of siRNA cations predominantly produced c- and y-type ions with minimal contributions of [a - Base] and w-type ions to the product ion current; the presence of only two complementary series of product ions in the IRMPD mass spectra simplified spectral interpretation. In addition, IRMPD produced high abundances of protonated nucleobases – [G+H]+, [A+H]+, and [C+H]+ - which were not detected in the CID mass spectra due to the low-mass cut-off associated with conventional CID in ion traps. CID and IRMPD using short irradiation times of duplex siRNA resulted in strand separation, similar to the dissociation trends observed for duplex DNA. With longer irradiation times, however, the individual single-strands underwent secondary dissociation to yield informative sequence ions not obtained by CID.

A mix of theoretical and applied research has provided insights into how recombination can be guided to more efficiently generate proteins and even organisms with altered functions.

DNA shuffling has proven to be a powerful technique for the directed evolution of proteins. A mix of theoretical and applied research has now provided insights into how recombination can be guided to more efficiently generate proteins and even organisms with altered functions.

We present a robust, sensitive, fluorescent or radio label-free self-assembled optical diffraction biosensor that utilizes rolling circle amplification (RCA) and magnetic microbeads as a signal enhancement method. An aptamer-based sandwich assay was performed on microcontact-printed streptavidin arranged in 15-μm-wide alternating lines, and could specifically capture and detect platelet-derived growth factor B-chain (PDGF-BB). An aptamer served as a template for the ligation of a padlock probe and the circularized probe could in turn be used as a template for RCA. The concatameric RCA product hybridized to biotinylated oligonuclotides which then captured streptavidin-labeled magnetic beads. In consequence, the signal from the captured PDGF-BB was amplified via the concatameric RCA product, and the diffraction gratings on the printed areas produced varying intensities of diffraction modes. The detected diffraction intensity and the density of the microbeads on the surface varied as a function of PDGF-BB concentration. Our results demonstrate a robust biosensing platform that is easy to construct and use, and devoid of fluorescence microscopy. The self-assembled bead patterns allow both a visual analysis of the molecular binding events under an ordinary bright-field microscope and serve as a diffraction grating biosensor.

Bacteria are social organisms that display distinct behaviors/phenotypes when present in groups. These behaviors include the abilities to construct antibiotic-resistant sessile biofilm communities and to communicate with small signaling molecules (quorum sensing [QS]). Our understanding of biofilms and QS arises primarily from in vitro studies of bacterial communities containing large numbers of cells, often greater than 108 bacteria; however, in nature, bacteria often reside in dense clusters (aggregates) consisting of significantly fewer cells. Indeed, bacterial clusters containing 101 to 105 cells are important for transmission of many bacterial pathogens. Here, we describe a versatile strategy for conducting mechanistic studies to interrogate the molecular processes controlling antibiotic resistance and QS-mediated virulence factor production in high-density bacterial clusters. This strategy involves enclosing a single bacterium within three-dimensional picoliter-scale microcavities (referred to as bacterial “lobster traps”) defined by walls that are permeable to nutrients, waste products, and other bioactive small molecules. Within these traps, bacteria divide normally into extremely dense (1012 cells/ml) clonal populations with final population sizes similar to that observed in naturally occurring bacterial clusters. Using these traps, we provide strong evidence that within low-cell-number/high-density bacterial clusters, QS is modulated not only by bacterial density but also by population size and flow rate of the surrounding medium. We also demonstrate that antibiotic resistance develops as cell density increases, with as few as ~150 confined bacteria exhibiting an antibiotic-resistant phenotype similar to biofilm bacteria. Together, these findings provide key insights into clinically relevant phenotypes in low-cell-number/high-density bacterial populations.

IMPORTANCE

Prokaryotes are social organisms capable of coordinated group behaviors, including the abilities to construct antibiotic-resistant biofilms and to communicate with small signaling molecules (quorum sensing [QS]). While there has been significant effort devoted to understanding biofilm formation and QS, few studies have examined these processes in high-density/low-cell-number populations. Such studies have clinical significance, as many infections are initiated by small bacterial populations (<105) that are organized into dense clusters. Here, we describe a technology for studying such bacterial populations in picoliter-sized porous cavities (referred to as bacterial “lobster traps”) capable of capturing a single bacterium and tracking growth and behavior in real time. We provide evidence that small changes in the size of the bacterial cluster as well as flow rate of the surrounding medium modulate QS in Pseudomonas aeruginosa. We also demonstrate that as few as ~150 confined bacteria are needed to exhibit an antibiotic-resistant phenotype similar to biofilm bacteria.

Summary

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

While a number of aminoacyl tRNA synthetase (aaRS):tRNA pairs have been engineered to alter or expand the genetic code, only the Methanococcus jannaschii tyrosyl tRNA synthetase and tRNA have been used extensively in bacteria, limiting the types and numbers of unnatural amino acids that can be utilized at any one time to expand the genetic code. In order to expand the number and type of aaRS/tRNA pairs available for engineering bacterial genetic codes, we have developed an orthogonal tryptophanyl tRNA synthetase and tRNA pair, derived from Saccharomyces cerevisiae. In the process of developing an amber suppressor tRNA, we discovered that the Escherichia coli lysyl tRNA synthetase was responsible for misacylating the initial amber suppressor version of the yeast tryptophanyl tRNA. It was discovered that modification of the G:C content of the anticodon stem and therefore reducing the structural flexibility of this stem eliminated misacylation by the E. coli lysyl tRNA synthetase, and led to the development of a functional, orthogonal suppressor pair that should prove useful for the incorporation of bulky, unnatural amino acids into the genetic code. Our results provide insight into the role of tRNA flexibility in molecular recognition and the engineering and evolution of tRNA specificity.

Myelin of the adult central nervous system (CNS) is one of the major sources of inhibitors of axon regeneration following injury. The three known myelin-derived inhibitors (Nogo, MAG, and OMgp) bind with high affinity to the Nogo-66 receptor (NgR) on axons and limit neurite outgrowth. Here we show that RNA aptamers can be generated that bind with high affinity to NgR, compete with myelin-derived inhibitors for binding to NgR, and promote axon elongation of neurons in vitro even in the presence of these inhibitors. Aptamers may have key advantages over protein antagonists, including low immunogenicity and the possibility of ready modification during chemical synthesis for stability, signaling, or immobilization. This first demonstration that aptamers can directly influence neuronal function suggests that aptamers may prove useful for not only healing spinal cord and other neuronal damage, but may be more generally useful as neuromodulators.

Because of the increasing recognition of the importance of non-coding RNAs in gene regulation, there is considerable interest in identifying RNA motifs in genomic data. In a recent report in BMC Genomics, Breaker and colleagues describe a new algorithm for identifying functional noncoding RNAs in metagenomic sequences of marine organisms, a strategy that may be particularly effective for discovering new and unique riboswitches.

Nucleic acid sensor elements are proving increasingly useful in biotechnology and biomedical applications. A number of ligand-sensing, conformational-switching ribozymes (also known as allosteric ribozymes or aptazymes) have been generated by some combination of directed evolution or rational design. Such sensor elements typically fuse a molecular recognition domain (aptamer) with a catalytic signal generator (ribozyme). Although the rational design of aptazymes has begun to be explored, the relationships between the thermodynamics of aptazyme conformational changes and aptazyme performance in vitro and in vivo have not been examined in a quantitative framework. We have therefore developed a quantitative and predictive model for aptazymes as biosensors in vitro and as riboswitches in vivo. In the process, we have identified key relationships (or dimensionless parameters) that dictate aptazyme performance, and in consequence, established equations for precisely engineering aptazyme function. In particular, our analysis quantifies the intrinsic trade-off between ligand sensitivity and the dynamic range of activity. We were also able to determine how in vivo parameters, such as mRNA degradation rates, impact the design and function of aptazymes when used as riboswitches. Using this theoretical framework we were able to achieve quantitative agreement between our models and published data. In consequence, we are able to suggest experimental guidelines for quantitatively predicting the performance of aptazyme-based riboswitches. By identifying factors that limit the performance of previously published systems we were able to generate immediately testable hypotheses for their improvement. The robust theoretical framework and identified optimization parameters should now enable the precision design of aptazymes for biotechnological and clinical applications.

Author Summary

Aptamers are nucleic acids that bind their cognate ligands (ranging from metal ions to small molecules to proteins) specifically and tightly. Through rational design and/or directed evolution, aptamers can be engineered into allosteric nucleic acids whose conformations can be regulated by their ligands. Aptamer beacons, aptazymes, and riboswitches all undergo ligand-dependent conformational changes, and have been adapted to signal the concentration of their ligands. However, there is currently no model that can be used to predict how the energetics of conformational change affects signaling, either in vitro or in vivo. We have developed a model that identifies what parameters can be optimized to best yield signals. By focusing on these parameters, it should be possible to more readily design or select more effective conformation-switching nucleic acid biosensors.

We have developed a novel method that utilizes conformation-switching aptamers for real-time PCR analysis of protein analytes. The aptamers have been designed so that they assume one secondary structure in the absence of a protein analyte, and a different secondary structure in the presence of a protein such as thrombin or PDGF. The protein-bound structure in turn assembles a ligation junction for the addition of a real-time PCR primer. Protein concentrations could be specifically detected into the picomolar range, even in the presence of cell lysates. The method has advantages relative to both immunoPCR (since no signal is produced by background binding) and to the proximity ligation assay (PLA; since only one epitope on a protein surface must be bound, rather than two).

Summary

Stochastic fluctuations (noise) in gene expression can cause members of otherwise genetically identical populations to display drastically different phenotypes. An understanding of the sources of noise and the strategies cells employ to function reliably despite noise is proving to be increasingly important in describing the behavior of natural organisms and will be essential for the engineering of synthetic biological systems. Here we describe the design of synthetic constructs, termed ribosome competing RNAs (rcRNAs), as a means to rationally perturb noise in cellular gene expression. We find that noise in gene expression increases in a manner proportional to the ability of an rcRNA to compete for the cellular ribosome pool. We then demonstrate that operons significantly buffer noise between coexpressed genes in a natural cellular background and can even reduce the level of rcRNA enhanced noise. These results demonstrate that engineering exogenous genetic elements can significantly affect the natural noise profile of a living cell and, importantly, that operons are a facile genetic strategy for buffering against noise.

The power of electronic computation is due in part to the development of modular gate structures that can be coupled to carry out sophisticated logical operations and whose performance can be readily modelled. However, the equivalences between electronic and biochemical operations are far from obvious. In order to help cross between these disciplines, we develop an analogy between complementary metal oxide semiconductor and transcriptional logic gates. We surmise that these transcriptional logic gates might prove to be useful in amorphous computations and model the abilities of immobilized gates to form patterns. Finally, to begin to implement these computations, we design unique hairpin transcriptional gates and then characterize these gates in a binary latch similar to that already demonstrated by Kim et al. (Kim, White & Winfree 2006 Mol. Syst. Biol. 2, 68 (doi:10.1038/msb4100099)). The hairpin transcriptional gates are uniquely suited to the design of a complementary NAND gate that can serve as an underlying basis of molecular computing that can output matter rather than electronic information.

Aptamers are nucleic acid molecules selected in vitro to bind a particular ligand. While numerous experimental studies have examined the sequences, structures, and functions of individual aptamers, considerably fewer studies have applied bioinformatics approaches to try to infer more general principles from these individual studies. We have used a large Aptamer Database to parse the contributions of both random and constant regions to the secondary structures of more than 2000 aptamers. We find that the constant, primer-binding regions do not, in general, contribute significantly to aptamer structures. These results suggest that (a) binding function is not contributed to nor constrained by constant regions; (b) in consequence, the landscape of functional binding sequences is sparse but robust, favoring scenarios for short, functional nucleic acid sequences near origins; and (c) many pool designs for the selection of aptamers are likely to prove robust.

Polarizing cells extensively restructure cellular components in a spatially and temporally coupled manner along the major axis of cellular extension. Budding yeast are a useful model of polarized growth, helping to define many molecular components of this conserved process. Besides budding, yeast cells also differentiate upon treatment with pheromone from the opposite mating type, forming a mating projection (the ‘shmoo’) by directional restructuring of the cytoskeleton, localized vesicular transport and overall reorganization of the cytosol. To characterize the proteomic localization changes accompanying polarized growth, we developed and implemented a novel cell microarray-based imaging assay for measuring the spatial redistribution of a large fraction of the yeast proteome, and applied this assay to identify proteins localized along the mating projection following pheromone treatment. We further trained a machine learning algorithm to refine the cell imaging screen, identifying additional shmoo-localized proteins. In all, we identified 74 proteins that specifically localize to the mating projection, including previously uncharacterized proteins (Ycr043c, Ydr348c, Yer071c, Ymr295c, and Yor304c-a) and known polarization complexes such as the exocyst. Functional analysis of these proteins, coupled with quantitative analysis of individual organelle movements during shmoo formation, suggests a model in which the basic machinery for cell polarization is generally conserved between processes forming the bud and the shmoo, with a distinct subset of proteins used only for shmoo formation. The net effect is a defined ordering of major organelles along the polarization axis, with specific proteins implicated at the proximal growth tip.

Upon sensing mating pheromone, budding yeast cells form a mating projection (the ‘shmoo’) that serves as a model for polarized cell growth, involving cytoskeletal/cytosolic restructuring and directed vesicular transport. We developed a cell microarray-based imaging assay for measuring localization of the yeast proteome during polarized growth. We find major organelles ordered along the polarization axis, localize 74 proteins to the growth tip, and observe adaptive reuse of general polarization machinery.